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Catching Growth Determinants with the Adaptive Lasso

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  • Ulrike Schneider
  • Martin Wagner

Abstract

This paper uses the adaptive Lasso estimator to determine the variables important for economic growth. The adaptive Lasso estimator is a computationally very simple procedure that can perform at the same time model selection and consistent parameter estimation. The methodology is applied to three data sets, the data used in Sala-i-Martin et al. (2004), in Fernandez et al. (2001) and a data set for the regions in the European Union. The results for the former two data sets are similar in several respects to those found in the published papers, yet are obtained at a negligible fraction of computational cost. Furthermore, the results for the European regional data highlight the importance of human capital for economic growth.

Suggested Citation

  • Ulrike Schneider & Martin Wagner, 2009. "Catching Growth Determinants with the Adaptive Lasso," wiiw Working Papers 55, The Vienna Institute for International Economic Studies, wiiw.
    • Handle: RePEc:wii:wpaper:55
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    References listed on IDEAS

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    1. Durlauf, Steven N. & Johnson, Paul A. & Temple, Jonathan R.W., 2005. "Growth Econometrics," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.),Handbook of Economic Growth, edition 1, volume 1, chapter 8, pages 555-677, Elsevier.
    2. Magnus, J.R. & Powell, O.R. & Prüfer, P., 2008. "A Comparison of Two Averaging Techniques with an Application to Growth Empirics," Other publications TiSEM 0392dffa-51e0-4bc9-9644-f, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Savin Ivan, 2013. "A Comparative Study of the Lasso-type and Heuristic Model Selection Methods," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(4), pages 526-549, August.
    2. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
    3. Marcos Sanso-Navarro & María Vera-Cabello, 2015. "Non-linearities in regional growth: A non-parametric approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 19-38, November.
    4. Hajek, Petr & Henriques, Roberto & Hajkova, Veronika, 2014. "Visualising components of regional innovation systems using self-organizing maps—Evidence from European regions," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 197-214.
    5. Wagner Martin & Hlouskova Jaroslava, 2015. "Growth Regressions, Principal Components Augmented Regressions and Frequentist Model Averaging," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 642-662, December.
    6. Ofori, Isaac Kwesi, 2021. "Catching The Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning," EconStor Preprints 235482, ZBW - Leibniz Information Centre for Economics.
    7. Gross, Marco, 2011. "Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession," Working Paper Series 1286, European Central Bank.
    8. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    9. Jesus regstdpo-Cuaresma & Neil Foster & Robert Stehrer, 2011. "Determinants of Regional Economic Growth by Quantile," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 809-826.
    10. Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," Papers 1606.00142, arXiv.org.
    11. Wagner, Martin & Hlouskova, Jaroslava, 2009. "Growth Regressions, Principal Components and Frequentist Model Averaging," Economics Series 236, Institute for Advanced Studies.
    12. Wagner Martin & Zeileis Achim, 2019. "Heterogeneity and Spatial Dependence of Regional Growth in the EU: A Recursive Partitioning Approach," German Economic Review, De Gruyter, vol. 20(1), pages 67-82, February.
    13. Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2016. "Unveiling covariate inclusion structures in economic growth regressions using latent class analysis," European Economic Review, Elsevier, vol. 81(C), pages 189-202.
    14. Christoph Hanck, 2016. "I just ran two trillion regressions," Economics Bulletin, AccessEcon, vol. 36(4), pages 2037-2042.
    15. Piotr Wójcik & Bartłomiej Wieczorek, 2020. "We have just explained real convergence factors using machine learning," Working Papers 2020-38, Faculty of Economic Sciences, University of Warsaw.
    16. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2022. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from The Lasso Regularization and Inferential Techniques," Working Papers of the African Governance and Development Institute. 22/061, African Governance and Development Institute..
    17. Martin Wagner & Achim Zeileis, 2012. "Heterogeneity of Regional Growth in the European Union," Working Papers 2012-20, Faculty of Economics and Statistics, Universität Innsbruck.
    18. Jaroslava Hlouskova & Martin Wagner, 2013. "The Determinants of Long-Run Economic Growth: A Conceptually and Computationally Simple Approach," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(IV), pages 445-492, December.

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    More about this item

    Keywords

    adaptive Lasso; economic convergence; growth regressions; model selection;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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